A machine learning approach to distinguish between knees without and with osteoarthritis using MRI-based radiomic features from tibial bone

Jukka Hirvasniemi,Sita Bierma-Zeinstra,Edwin H G Oei,Stefan Klein,Dieuwke Schiphof,Meike W Vernooij

doi:10.1007/s00330-021-07951-5

Abstract

ObjectivesOur aim was to assess the ability of semi-automatically extracted magnetic resonance imaging (MRI)–based radiomic features from tibial subchondral bone to distinguish between knees without and with osteoarthritis.MethodsThe right knees of 665 females from the population-based Rotterdam Study scanned with 1.5T MRI were analyzed. A fast imaging employing steady-state acquisition sequence was used for the quantitative bone analyses. Tibial bone was segmented using a method that combines multi-atlas and appearance models. Radiomic features related to the shape and texture were calculated from six volumes of interests (VOIs) in the proximal tibia. Machine learning–based Elastic Net models with 10-fold cross-validation were used to distinguish between knees without and with MRI Osteoarthritis Knee Score (MOAKS)–based tibiofemoral osteoarthritis. Performance of the covariate (age and body mass index), image features, and combined covariate + image features models were assessed using the area under the receiver operating characteristic curve (ROC AUC).ResultsOf 665 analyzed knees, 76 (11.4%) had osteoarthritis. An ROC AUC of 0.68 (95% confidence interval (CI): 0.60–0.75) was obtained using the covariate model. The image features model yielded an ROC AUC of 0.80 (CI: 0.73–0.87). The model that combined image features from all VOIs and covariates yielded an ROC AUC of 0.80 (CI: 0.73–0.87).ConclusionOur results suggest that radiomic features are useful imaging biomarkers of subchondral bone for the diagnosis of osteoarthritis. An advantage of assessing bone on MRI instead of on radiographs is that other tissues can be assessed simultaneously.Key Points• Subchondral bone plays a role in the osteoarthritis disease processes.• MRI radiomics is a potential method for quantifying changes in subchondral bone.• Semi-automatically extracted radiomic features of tibia differ between subjects without and with osteoarthritis.

Highlights

Osteoarthritis is the most common joint disease affecting over 40 million people in Europe [1]
When classifying knees without and with tibiofemoral osteoarthritis, ROC AUC and PR AUC of 0.80 and 0.46 were obtained with the model that combined image features from all volumes of interests (VOIs) and covariates (Table 2, Fig. 2)
The model that combined image features from the medial subchondral bone VOI with covariates had an ROC AUC of 0.80 and a PR AUC of 0.46 (Supplementary Figure 1)

Summary

Introduction

Osteoarthritis is the most common joint disease affecting over 40 million people in Europe [1]. It reduces the quality of life of an individual and imposes a large economic burden on the society, since the direct and indirect costs can be as high as 2.5% of the gross domestic product of a nation [2, 3]. Eur Radiol (2021) 31:8513–8521 tissues in the joint, e.g., causing progressive degeneration of articular cartilage and changes in the subchondral bone density and structure [4, 5]. The exact role of subchondral bone in the development and progression of osteoarthritis is still unclear [5]

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